Optimized Smart Grid Energy Procurement for LTE Networks Using Evolutionary Algorithms

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Energy efficiency aspects in cellular networks can significantly contribute in the reduction of the worldwide green- house gas emissions. The Base Station (BS) sleeping strategy has become a well-known technique to achieve energy savings by switching off redundant BSs mainly for lightly loaded networks. Besides, introducing renewable energies as alternative power sources becomes a real challenge to network operators. In this pa- per, we formulate an optimization problem that aims to maximize the profit of a Long-Term Evolution (LTE) cellular operator, and to minimize the CO 2 emissions in green wireless cellular networks simultaneously without affecting the desired Quality of Service (QoS). The BS sleeping strategy lends itself to an interesting implementation using several heuristic approaches such as the Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO). In this paper, we propose GA-based and PSO-based methods that reduces the energy consumption of BSs by not only shutting down underutilized BSs but also by optimizing the amounts of energy procured from different retailers (renewable energy and electricity retailers). A comparison with another previously proposed algorithm is also carried out to evaluate the performance and the computational complexity of the employed methods.